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import os
import torch
import argparse
import torch.nn as nn
import torch.nn.functional as F
import gradio as gr
import codecs
import numpy as np
import cv2
from PIL import Image
from model import PeekabooModel
from misc import load_config
from torchvision import transforms as T
NORMALIZE = T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
if __name__ == "__main__":
def inference(img_path):
# Load the image
with open(img_path, "rb") as f:
img = Image.open(f)
img = img.convert("RGB")
img_np = np.array(img)
# Preprocess
t = T.Compose([T.ToTensor(), NORMALIZE])
img_t = t(img)[None, :, :, :]
inputs = img_t.to(device)
# Forward step
print(f"Start Peekaboo prediction.")
with torch.no_grad():
preds = model(inputs, for_eval=True)
print(f"Done Peekaboo prediction.")
sigmoid = nn.Sigmoid()
h, w = img_t.shape[-2:]
preds_up = F.interpolate(
preds, scale_factor=model.vit_patch_size, mode="bilinear", align_corners=False
)[..., :h, :w]
preds_up = (sigmoid(preds_up.detach()) > 0.5).squeeze(0).float()
preds_up = preds_up.cpu().squeeze().numpy()
# Overlay predicted mask with input image
preds_up_np = (preds_up / np.max(preds_up) * 255).astype(np.uint8)
preds_up_np_3d = np.stack([preds_up_np, preds_up_np, preds_up_np], axis=-1)
combined_image = cv2.addWeighted(img_np, 0.5, preds_up_np_3d, 0.5, 0)
print(f"Output shape is {combined_image.shape}")
return combined_image
parser = argparse.ArgumentParser(
description="Evaluation of Peekaboo",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
"--img-path",
type=str,
default="data/examples/VOC_000030.jpg",
help="Image path.",
)
parser.add_argument(
"--model-weights",
type=str,
default="data/weights/peekaboo_decoder_weights_niter500.pt",
)
parser.add_argument(
"--config",
type=str,
default="configs/peekaboo_DUTS-TR.yaml",
)
parser.add_argument(
"--output-dir",
type=str,
default="outputs",
)
args = parser.parse_args()
# Configuration
config, _ = load_config(args.config)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load the model
model = PeekabooModel(
vit_model=config.model["pre_training"],
vit_arch=config.model["arch"],
vit_patch_size=config.model["patch_size"],
enc_type_feats=config.peekaboo["feats"],
)
# Load weights
model.decoder_load_weights(args.model_weights)
model.eval()
print(f"Model {args.model_weights} loaded correctly.")
# App
title = "PEEKABOO: Hiding Parts of an Image for Unsupervised Object Localization"
description = codecs.open("./media/description.html", "r", "utf-8").read()
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2407.17628' target='_blank'>PEEKABOO: Hiding Parts of an Image for Unsupervised Object Localization</a> | <a href='https://github.com/hasibzunair/peekaboo' target='_blank'>Github</a></p>"
gr.Interface(
inference,
gr.inputs.Image(type="filepath", label="Input Image"),
gr.outputs.Image(type="numpy", label="Predicted Output"),
examples=[
"./data/examples/a.jpeg",
"./data/examples/b.jpeg",
"./data/examples/c.jpeg",
"./data/examples/d.jpeg",
"./data/examples/e.jpeg"
],
title=title,
description=description,
article=article,
allow_flagging=False,
analytics_enabled=False,
).launch(debug=True, enable_queue=True)
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